On the Luv-Lee Problem in the Simulation of Orographic Precipitation G. Doms, J.-P. Schulz a, D. Majewski a, J. Förstner a, V. Galabov b 1. Spatial Distribution.

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Presentation transcript:

On the Luv-Lee Problem in the Simulation of Orographic Precipitation G. Doms, J.-P. Schulz a, D. Majewski a, J. Förstner a, V. Galabov b 1. Spatial Distribution of Precipitation in Southwest Germany 2. Formation of Stratiform Precipitation and Parameterization 3. Sensitivity to Seeder-Feeder and Prognostic Precipitation 4. Conclusions a) (DWD) b) National Institute of Meteorology and Hydrology, Bulgaria

Monthly mean precipitation amount for Germany/Switzerland June 2002 January 2003 OBS (74mm) LM (69mm) OBS (84mm) LM (80mm)

ObservationLM 00 UTC + 06h-30h Precipitation 20/02-21/02/2002 Operational LM

Working Hypothesis The erroneous spatial distribution of precipitation over mountainous terrain (mainly during wintertime) might be due to dynamical-numerical mechanisms over-estimation of the mountain wave amplitude in case of stable stratification and high wind speeds dynamical-microphysical mechanism over-estimation of precipitation enhancement by the seeder-feeder effect due to neglecting the horizontal and vertical advective transport of snow (and rain) in the present parameterization scheme

Parameterization of cloud microphysical processes Precipitation enhancement in mixed pase clouds: Bergeron-Findeisen process Seeder Feeder mechanism

7 km m/s 0°C 6 km 1 km 3 km Formation of precipitation over a mountain

Treatment of Precipitation in NWP Models Diagnostic Scheme Simplified budget equations for rain and snow precipitation fluxes Column equilibrium saves CPU time and core memory High accuracy at larger scales Standard in NWP models Prognostic Scheme Full 3D budget equations for rain and snow mixing ratios Computational expensive Requires a special numerical treatment of the sedimentation term due to CFL for fallout Necessary to account for horizontal and vertical transport in small-scale modelling (lee- side precipitation, life-cycle in convective clouds) Standard in CRMs

Calculation of trajectories for LM to estimate the drifting of snow Fall speed: 2 m/s Fall down to the melting zone  850 hPa

Experiments Re-run of PYREX-IOPs LM case studies with 28, 14 and 7 km grid-spacing Switch-off riming and accretional growth (sensitivity to seeder-feeder mechanism) LM case studies using the 2-time-level scheme with diagnostic and prognostic treatment of rain and snow (seeder-feeder cut-off)

24-h precipitation amount LM 7 kmLM 14 km LM 28 kmBeobachtung

24-h precipitation amount UTC + 06h-30h Operational LM LM without accrection and riming

Vertical cross sections at 48.4°N LM with drifting of precipitation 00 UTC + 15h Specific water content of snow Specific water content of rain [mg/kg] O km 2 km 6 km 4 km O km 2 km 6 km 4 km 0 km 440 km

Cross sections at 48.4°N Mean vertical motion [Pa/s] at 600 hPaPrecipitation [mm] 00 UTC + 06h-30h Black: Operational LM Red : LM with drifting of precipitation 0 km 440 km0 km 440 km

Precipitation 20/02-21/02/2002 Observation LM 00 UTC + 06h-30h LM with drifting of precipitation

24-h precipitation amount LM 00 UTC + 06h-30h LM-7km with 3-d transport of precipitation Operational LM (LM-7km) Observation

24-h precipitation amount Beobachtung LM 00 UTC + 06h-30h LM-7km with 3-d transport of precipitation Operational LM (LM-7km) LM 00 UTC + 06h-30h

Conclusions Gravity and mountain wave dynamics is well represented by the LM (PYREX) Lee-side distribution of precipitation is very sensitive to the seeder-feeder mechanism Including the 3-D transport of precipitation (in particular of snow) appears to significantly improve the distribution of precipitation on the upwind side and in the lee of mountains more case studies are necessary

prognostic treatment of rain and snow needs about 50% more computing time for the total LM, new numerics (2-time-level scheme) have to be optimized and tested thoroughly, as an intermediate step, the prognostic precipitation scheme will be implemented within the operational 3-TL integration scheme using a semi-Lagrangian transport scheme (2Q 2004)